DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.